Bibliographic record
Abstract
J C P H – Vol. 58, n 3 – juin 2005 180 has been launched this year to give secondyear pharmacy students CSHP’s Direct Patient Care Curriculum, a new student award, special educational symposia, and an opportunity to work at our head office. These initiatives all appear worthy, but how will we measure their tangible effects on pharmacy practice across Canada? To determine our Society’s success, we need to develop metrics that measure the profession’s growth in key areas. Yet before we decide what we need to measure, we must decide where we want to go. Therefore, CSHP must develop a vision for hospital pharmacy that is clear, well defined, and measurable, and then we must set metrics to track our progress. I am reminded of a phrase that Don Berwick used in his address to the Institute for Healthcare Improvement (IHI) this past December: “Some is not a number; soon is not a time.” Like IHI, we must set a defined target for hospital pharmacy to be achieved within a defined time frame and then take action to make it happen. It’s up to CSHP to answer the question, “What do we want hospital pharmacy to look like in 2010?” Then we all need to make it a reality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.101 | 0.032 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".